63.6k citations · 66.4k across the 51 of their papers we have counts for
6 papers · 1 filter
Improving accuracy and power with transfer learning using a meta-analytic database
Yannick Schwartz, Gaël Varoquaux, Christophe Pallier +3
Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investiga…
On spatial selectivity and prediction across conditions with fMRI
Yannick Schwartz, Gaël Varoquaux, Bertrand Thirion
Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of…
Improved brain pattern recovery through ranking approaches
Fabian Pedregosa, Alexandre Gramfort, Gaël Varoquaux +3
Inferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GL…
Learning to rank from medical imaging data
Fabian Pedregosa, Alexandre Gramfort, Gaël Varoquaux +3
Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted v…
Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks?
Gaël Varoquaux, Alexandre Gramfort, Jean Baptiste Poline +1
Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynam…
Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort +16
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses…